Mercury is a Distributed Metadata Management, Data Discovery and Access System.[1] It is a scientific data search system to capture and manage biogeochemical and ecological data in support of the National Aeronautics and Space Administration's (NASA's) Earth science programs. Mercury was originally developed for NASA, but the consortium is now supported by NASA, U.S. Geological Survey and U.S. Department of Energy. Ongoing development of Mercury is done through an informal consortium at Oak Ridge National Laboratory DAAC
Mercury is a part of Oak Ridge National Laboratory DAAC (ORNL DAAC). The Oak Ridge National Laboratory - Distributed Active Archive Center (ORNL DAAC) for Biogeochemical Dynamics is operated by the ORNL Environmental Sciences Division (ESD) as part of the National Aeronautics and Space Administration's (NASA) Earth Science Data and Information System (ESDIS) project.[2] The ORNL DAAC archives data and products related to the biological, geological, and chemical components of the Earth's environment.
Contents
1Mission
2See also
3References
4External links
Mission
Mercury supports data archiving, data discovery through various search strategies (text string, fielded, spatial, temporal), data reuse, and longer-term scientific digital data stewardship, and supports a range of recognized data exchange and interoperability protocols and supports various metadata standards including XML, Z39.50, FGDC, Dublin Core, Darwin Core, Ecological Metadata Language, and ISO. Mercury also uses OAI-PMH to index metadata records from Global Change Master Directory (GCMD) and redistribute them other data providers[3]
See also
Distributed Active Archive Center
Oak Ridge National Laboratory DAAC
References
^R. Devarakonda; G. Palanisamy; B. Wilson; J. Green, "Mercury: reusable metadata management, data discovery and access system", Earth Science Informatics, Springer Berlin / Heidelberg, 3 (1): 87–94, doi:10.1007/s12145-010-0050-7
^NASA Earth System Science Data and Services: About the Centers. "[1]"
^R. Devarakonda; G. Palanisamy; J. Green; B. Wilson, "Data sharing and retrieval uses OAI-PMH", Earth Science Informatics, Springer Berlin / Heidelberg, doi:10.1007/s12145-010-0073-0
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1 I having trouble getting my ResourceDictionary.MergedDictionaries to load from app.xaml. My WPF app has a static class with a Main defined and startup object set to it. Within Main I created an instance of App and run it. The override OnStartup fires and the mainwindow.cs InitializeComponent gives the error "Message "Cannot find resource named 'MaterialDesignFloatingActionMiniAccentButton'. If I put the resources in the mainwindow.xaml everything is fine, but I wanted them to load at the app level so I they are not in each page. Any help appreciated. public partial class App protected override void OnStartup(StartupEventArgs e) base.OnStartup(e); var app = new MainWindow(); var context = new MainWindowViewModel(); app.DataContext = context; app.Show(); from the Main.. var app = new App(); app.Run(); app.xaml.. <Application x:Class="GS.Server.App" xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation" xmlns:...
up vote 2 down vote favorite There is a clear pattern that show for two separate subsets (set of columns); If one value is missing in a column, values of other columns in the same subset are missing for any row. Here is a visualization of missing data My tries up until now, I used ycimpute library to learn from other values, and applied Iterforest. I noted, score of Logistic regression is so weak (0.6) and thought Iterforest might not learn enough or anyway, except from outer subset which might not be enough? for example the subset with 11 columns might learn from the other columns but not from within it's members, and the same goes for the subset with four columns. This bar plot show better quantity of missings So of course, dealing with missings is better than dropping rows because It would affect my prediction which does contain the same missings quantity relatively. Any better way to deal with these ? [EDIT] The nullity pattern is confirmed: machine-learning cor...
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